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https://github.com/wangzheng17/awesome-causal-vision

A curated list of research papers in exploring causality in vision. Link to the code if available is also present.
https://github.com/wangzheng17/awesome-causal-vision

List: awesome-causal-vision

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A curated list of research papers in exploring causality in vision. Link to the code if available is also present.

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# Awesome Causal Vision



A curated list of research papers in exploring causality in vision. Link to the code if available is also present.

I might have missed some paper(s) or added some irrelevant paper(s). Feel free to open an issue in that case. I will go through the paper and then add / remove it. 



## Paper



1. **Discovering causal signals in images.** Lopez-Paz, D., Nishihara, R., Chintala, S., Scholkopf, B. and Bottou, L., 2017. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Lopez-Paz_Discovering_Causal_Signals_CVPR_2017_paper.pdf)



2. **Causally regularized learning with agnostic data selection bias.** Shen, Z., Cui, P., Kuang, K., Li, B. and Chen, P., 2018. Proceedings of the 26th ACM international conference on Multimedia. [[Paper]](https://arxiv.org/pdf/1708.06656.pdf)



3. **Causal reasoning from meta-reinforcement learning.** Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M. and Kurth-Nelson, Z., 2019. arXiv preprint arXiv:1901.08162. [[Paper]](https://arxiv.org/pdf/1901.08162.pdf)



4. **Unbiased scene graph generation from biased training.** Tang, K., Niu, Y., Huang, J., Shi, J. and Zhang, H., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.pdf)



5. **Visual commonsense r-cnn**. Wang, T., Huang, J., Zhang, H. and Sun, Q., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Visual_Commonsense_R-CNN_CVPR_2020_paper.pdf)



6. **Two causal principles for improving visual dialog.** Qi, J., Niu, Y., Huang, J. and Zhang, H., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qi_Two_Causal_Principles_for_Improving_Visual_Dialog_CVPR_2020_paper.pdf)



7. **Counterfactual samples synthesizing for robust visual question answering.** Chen, L., Yan, X., Xiao, J., Zhang, H., Pu, S. and Zhuang, Y., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Counterfactual_Samples_Synthesizing_for_Robust_Visual_Question_Answering_CVPR_2020_paper.pdf)



8. **Counterfactual VQA: A Cause-Effect Look at Language Bias.** Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X.S. and Wen, J.R., 2020. arXiv preprint arXiv:2006.04315.[[Paper]](https://arxiv.org/pdf/2006.04315.pdf)



9. **Counterfactual vision and language learning.** Abbasnejad, E., Teney, D., Parvaneh, A., Shi, J. and Hengel, A.V.D., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.pdf)



10. **Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling.** Fu, T.J., Wang, X., Peterson, M., Grafton, S., Eckstein, M. and Wang, W.Y., 2019. arXiv preprint arXiv:1911.07308. [[Paper]](https://arxiv.org/pdf/1911.07308.pdf)



11. **Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing.** Agarwal, V., Shetty, R. and Fritz, M., 2020. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Agarwal_Towards_Causal_VQA_Revealing_and_Reducing_Spurious_Correlations_by_Invariant_CVPR_2020_paper.pdf)



12. **Robust Neural Network for Causal Invariant Features Extraction.** Zeng, S., Zhang, P., Charles, D., Manavoglu, E., & Kiciman, E. 2019. NIPS workshop. [[Paper]](https://cpb-us-w2.wpmucdn.com/sites.coecis.cornell.edu/dist/a/238/files/2019/12/Id_98_final.pdf)



13. **DeVLBert: Learning Deconfounded Visio-Linguistic Representations.** Zhang, S., Jiang, T., Wang, T., Kuang, K., Zhao, Z., Zhu, J., Yu, J., Yang, H. and Wu, F., 2020. arXiv preprint arXiv:2008.06884. [[Paper]](https://arxiv.org/pdf/2008.06884.pdf)



## Datasets

1. **Clevrer: Collision events for video representation and reasoning.** Yi, K., Gan, C., Li, Y., Kohli, P., Wu, J., Torralba, A. and Tenenbaum, J.B., 2019. arXiv preprint arXiv:1910.01442. [[Paper]](https://arxiv.org/pdf/1910.01442.pdf) [[Project]](http://clevrer.csail.mit.edu/)



## Survey

1. **A survey of learning causality with data: Problems and methods.** Guo, R., Cheng, L., Li, J., Hahn, P.R. and Liu, H., 2020. ACM Computing Surveys (CSUR), 53(4), pp.1-37. [[Paper]](https://arxiv.org/pdf/1809.09337.pdf)



2. **Causal Inference.** Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., Huang, H., Ding, P., Miao, W. and Jiang, Z., 2020. Engineering, 6(3), pp.253-263. [[Paper]](https://www.sciencedirect.com/science/article/pii/S2095809919305235)



## Groups

1. Microsoft Causality and Machine Learning Group [[Link]](https://www.microsoft.com/en-us/research/group/causal-inference/)



## Causality Books



1. Interpretation and identification of causal mediation. Judea Pearl, 2014. [pdf](https://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf)

2. (book) The Book of Why. Judea Pearl, 2018. [[onedrive]][21]

3. (book) The Book of Why(中文版). Judea Pearl & Dana Mackenzie, 江⽣ & 于华 译, 2018. [[onedrive]][22]

4. (book) Causality: Models, Reasoning, and Inference(2nd Edition). Judea Pearl, 2009. [[onedrive]][23]

5. (book) Causal inference in statistics: An overview. Judea Pearl, on Statistics Surveys, 2009. [[onedrive]][24]

6. (book) 因果推断简介. 丁鹏(北京大学). [[onedrive]][25]

7. (book) Causal Inference - What If. Miguel A. Hernán & James M. Robins, 2019. [[onedrive]][26]

8. (book) Elements of Causal Inference: Foundations and Learning Algorithms. MIT, 2020. [[onedrive]][27]

9. (book) Introduction to Causal Inference: from a Machine Learning Perspective. Brady Neal, Course Lecture Notes, 2020. [[onedrive]][35]



## Causality PPT



1. KDD 2020 Tutorial - Causal Inference and Stable Learning. [[ppt]][36]

2. MLSS 2020 - Causility. [[onedrive]][37]

3. MLSS 2020 - Causal Inference II. [[onedrive]][38]



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